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Data Version Control for Data Lakes: Handling the Changes in Large Scale Data Version Control for Data Lakes: Handling the Changes in Large Scale
In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across... Data Version Control for Data Lakes: Handling the Changes in Large Scale

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. As data lakes gain prominence as a preferred solution for storing and processing enormous datasets, the need for effective data version control mechanisms becomes increasingly evident. 

In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management.

Understanding Data Lakes

A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format. Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition. As a result, data lakes can accommodate vast volumes of data from different sources, providing a cost-effective and scalable solution for handling big data.

Before we address the questions, ‘What is data version control?’ and ‘Why is it important for data lakes?’, we will discuss the key characteristics of data lakes.

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Schema-on-Read vs. Schema-on-Write

Data lakes follow the ‘Schema-on-Read’ approach, which means data is stored in its raw form, and schemas are applied at the time of data consumption. In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database.

Flexibility and Agility

Data lakes provide flexibility, enabling organizations to store diverse data types without worrying about immediate data modeling. This allows data scientists, analysts, and other stakeholders to perform exploratory analyses and derive insights without prior knowledge of the data structure.

Cost-Efficiency

By leveraging cost-effective storage solutions like the Hadoop Distributed File System (HDFS) or cloud-based storage, data lakes can handle large-scale data without incurring prohibitive costs. This is particularly advantageous when dealing with exponentially growing data volumes.

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Data Lakes vs. Data Warehouses and Relational Databases

It is essential to distinguish data lakes from data warehouses and relational databases, as each serves different purposes and has distinct characteristics.

Data Warehouses

Some key characteristics of data warehouses are as follows:

  • Data Type: Data warehouses primarily store structured data that has undergone ETL (Extract, Transform, Load) processing to conform to a specific schema.
  • Schema Enforcement: Data warehouses use a “schema-on-write” approach. Data must be transformed and structured before loading, ensuring data consistency and quality.
  • Processing: Data warehouses employ massively parallel processing (MPP) for quick query performance. They are optimized for complex analytical queries and reporting.
  • Storage Optimization: Data warehouses use columnar storage formats and indexing to enhance query performance and data compression.
  • Use Cases: Data warehouses are tailored for business analysts, decision-makers, and executives who require fast, reliable access to structured data for reporting, business intelligence, and strategic decision-making.

In summary, data lakes prioritize data variety and exploration, making them suitable for scenarios where the data landscape is evolving rapidly, and the initial data structure might not be well-defined also data lakes are more suitable for storing diverse and raw data for exploratory analysis, while data warehouses focus on structured data, ensuring data quality and enabling efficient querying for business-critical operations like business intelligence and reporting.

Relational Databases

Some key characteristics of relational databases are as follows:

  • Data Structure: Relational databases store structured data in rows and columns, where data types and relationships are defined by a schema before data is inserted.
  • Schema Enforcement: Relational databases use a “schema-on-write” approach, where data must adhere to a predefined schema before it can be inserted. This ensures data consistency and integrity.
  • Processing: Relational databases are optimized for transactional processing and structured queries using SQL. They excel at managing structured data and supporting ACID (Atomicity, Consistency, Isolation, Durability) transactions.
  • Scalability: Relational databases can scale vertically by upgrading hardware, but horizontal scaling can be more challenging due to the need to maintain data integrity and relationships.
  • Use Cases: Relational databases are commonly used for applications requiring structured data management, such as customer relationship management (CRM), enterprise resource planning (ERP), and online transaction processing (OLTP) systems.

Data lakes are designed for storing and processing diverse and raw data, making them suitable for exploratory analysis and big data processing. Relational databases are optimized for structured data with well-defined schemas, making them suitable for transactional applications and structured querying.

The Importance of Data Version Control in Data Lakes

As data lakes become the backbone of modern data infrastructures, the management of data changes and version control becomes a critical challenge. Data version control refers to the ability to track, manage, and audit changes made to datasets over time. This is particularly vital in data lakes for the following reasons.

Data Volume and Diversity

Data lakes often contain vast and diverse datasets from various sources, with updates and additions occurring continuously. Managing these changes efficiently is crucial for maintaining data consistency and accuracy.

Collaborative Data Exploration

In data lakes, multiple teams and stakeholders collaboratively explore data to derive insights. Without proper version control, different users may inadvertently overwrite or modify data, leading to potential data integrity issues and confusion.

Auditing and Compliance

In regulated industries or environments with strict data governance requirements, data version control is essential for tracking changes, understanding data lineage, and ensuring compliance with regulations.


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Handling Changes at Scale with Data Version Control

To effectively handle changes at scale in data lakes, robust data version control mechanisms must be implemented. Here are some essential strategies:

  • Time-Stamped Snapshots: Maintaining time-stamped snapshots of the data allows for a historical view of changes made over time. These snapshots can be used to roll back to a previous state or track data lineage.
  • Metadata Management: Tracking metadata, such as data schema, data sources, and data transformation processes, aids in understanding the evolution of datasets and the context of changes.
  • Access Controls and Permissions: Implementing fine-grained access controls and permissions ensures that only authorized users can make changes to specific datasets, reducing the risk of unauthorized modifications.
  • Change Tracking and Notifications: Setting up change tracking mechanisms and notifications alerts stakeholders about data modifications, ensuring transparency and awareness.
  • Automated Testing and Validation: Automated testing and validation procedures help detect and rectify any anomalies or inconsistencies resulting from data changes.

Conclusion

Data lakes have revolutionized the way organizations manage and analyze large-scale data. Their ability to store diverse data types without predefined schemas makes them highly flexible and cost-efficient. However, managing changes in data lakes requires careful attention to ensure data consistency, accuracy, and compliance. 

Data version control plays a crucial role in addressing these challenges, enabling organizations to handle changes at scale and derive valuable insights from their data lakes with confidence and reliability. By implementing robust version control mechanisms and following best practices, businesses can leverage data lakes to their full potential, driving innovation and informed decision-making.

About Author –

Kruti Chapaneri is an aspiring software engineer and tech writer with a strong interest in the intersection of technology and business. She is excited to use her writing skills to help businesses grow and succeed online in the competitive market. You can connect with her on Linkedin.

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